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Semantic Technologies in the Financial Services Industry Presented by Drew Warren and George Roth

Industry Participants. BanksBroker/DealersInvestment Management FirmsInsurance CompaniesRegulatory AuthoritiesSystem and Data VendorsIndustry Utilities - DTCC. Investment Industry. Clients

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Semantic Technologies in the Financial Services Industry Presented by Drew Warren and George Roth

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    1. Semantic Technologies in the Financial Services Industry Presented by Drew Warren and George Roth San Jose, June, 2009

    2. Industry Participants Banks Broker/Dealers Investment Management Firms Insurance Companies Regulatory Authorities System and Data Vendors Industry Utilities - DTCC

    3. Investment Industry Clients – Retail and Institutional Investment Managers Custodians Broker/Dealers

    4. Investment Types Equities Fixed Income Corporate Government Municipal MBS/CMO……CDS

    5. Relationship Between Industry Members

    6. Introducing Semantic Technologies NOBODY CARES Hard Times – No Discretionary Projects Solve a Problem – Reduce Costs (Dramatically) Leverage an Opportunity – Generate Revenue Provide an Edge – Risk Management

    7. Obstacles Lack of Funds Approved Vendors Unproven/Unapproved Technology IT Standard Roadblocks

    8. Path to Success Baby Steps Solving Specific Problems High Return Less People – Same Work Diminished Expertise New Ways to Generate Revenue Gradual Introduction of Semantic Technologies and the Capabilities/Benefits They Provide

    9. Semantic Applications Semantic Technology applications overview Data Integration Semi Structured Documents analysis Determine unknown information connections Sharing knowledge Other applications Semantic Technology challenges The future

    10. Terms – no common vocabulary Semantic WEB = WWW Internet - false Semantic – RDF – Ontology - false Semantic = Extraction of the meaning where doesn’t explicitly exist in order to be understandable by machines The Semantic WEB is not the WWW – is the “complex web of relationships between things used to describe the meaning”

    11. Semantic = Meaning Gives meaning through relationships Building bloc – statements The statements describe: concepts, logic, restrictions and individuals (instances) WWW is for human consumption Semantic WEB – for machines Relationships: definitions, associations, aggregations and restrictions

    12. ST: Gives meaning through relationships

    13. Comparison of WWW and SW

    14. Semantic Applications Overview Are used to solve complicated problems All problems could be solved manually or with conventional applications but with a lot of effort – time extensive The Semantic WEB core idea is to “teach” the machine to “mimic” the human reasoning – simplistic approach

    15. Semantic Applications Samples Application Categories: Data Integration of heterogeneous data silos Semi Structured Documents Interpretation Unknown information connections detection through inference (reasoning) Knowledge management and sharing Others…

    16. Data Integration of heterogeneous data silos

    17. Comparison of Relational DB and Knowledgebase

    18. Alternative to Data Warehouse Why is better (Howard Greenblat – Metatomix): Leaves the data in place Much more flexible (no ETL) Extensible in time The structure doesn’t have to be known from the beginning The structure can be enhanced by needs and when the experience is accumulated

    19. Semi Structured Data Interpretation

    20. Unknown information connections detection through inference

    21. Unknown Information Connection detection through inference

    22. Knowledge Management and Sharing Working with ontologies Decoupling knowledge model from application Foundational ontologies http://watson.kmi.open.ac.uk/WatsonWUI/ Ontology Engineers – DBA -> Ontology Engineer Combine multiple ontologies

    23. Ontologies are complicated !!!! Start small Ontology = Politics Develop in time – focus on small areas Enterprise Ontology – “Heaven” Who needs it ? “Ontology repository” “Ontology Wizards” Semantic Arts

    24. Other Contextual Advertising Market Sentiment Analysis Combining the internal data with external data Blog interpretation Etc.

    25. Semantic Applications Challenges Sales Convince those who don’t trust the new stuff How to sell How not to sell Emerging technology Tendency to do complicated stuff (do not start with ontologies )

    26. The future Semantic Platforms will become a commodity (e.g. Metatomix) Semantic classifiers will be added to browsers RDF data stores will be added to each normal database Public ontology repositories (ontologies cannot develop without being Open Source) www.openontologyrepository.org

    27. Books and other info sources Semantic WEB Programming ISBN 978-0-470-41801-7 Semantic WEB for the Working Ontologist ISBN 978-0-12-373556-0 The Text Mining Hand Book ISBN ISBN 978-0-521-83657-9 www.twine.com Ontology tutorials (pizza, wine)

    28. Contact Info: Drew Warren CEO Recognos Financial, New York, USA dwarren@recognosfinancial.com George Roth CEO Recognos Inc., San Francisco, CA, USA groth@recognos.com www.recognos.com

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